GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization

Shengmin Piao; Sanghyun Park

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arXiv · 2605.27934

GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization

Shengmin Piao·Sanghyun Park

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Abstract

Reinforcement learning with verifiable rewards improves language model reasoning, but its reliance on domain-specific verifiers, sparse outcome rewards, and coarse-grained credit assignment limits its applicability. We introduce GeneralThinker, an on-policy framework that reformulates reasoning supervision as dense answer-conditioned optimization, enabling response-level evaluation and token-level credit assignment without domain-specific verifiers. GeneralThinker evaluates generated reasoning trajectories using the likelihood of the ground-truth answer and derives token-wise compatibility signals for fine-grained credit assignment. To stabilize optimization, it constrains token-level updates through clipping and direction-preserving modulation. Across 11 benchmarks spanning mathematics, STEM, and general reasoning, GeneralThinker achieves the best average performance. Further analyses show that uncontrolled token-level modulation can destabilize training, whereas controlled modulation makes fine-grained credit assignment consistently effective.

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Shengmin Piao, Sanghyun Park. 2026-05-27. GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization. https://arxiv.org/abs/2605.27934

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